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Purpose This study aimed to establish and validate a nomogram for predicting overall survival (OS) in young non-metastatic rectal cancer (RC) patients after curative resection. Methods Young RC patients (under 50 years of age) from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Those patients randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. The independent prognostic factors for OS were identified by univariate and multivariate Cox regression analysis. A nomogram model was built based on the independent prognostic variables and was evaluated by concordance index (C-index), receiver operating characteristics (ROC) curves, calibration plot, and decision curve analysis (DCA). Results A total number of 3026 young RC patients were extracted from SEER database. OS nomogram was constructed based on race, histological type, tumor grade, T stage, N stage, carcinoembryonic antigen (CEA) level, and number of lymph nodes (LN) examined. C-index, ROC curves, calibration plot, and DCA curves presented satisfactory performance of the above nomogram in predicting the prognosis of young non-metastatic RC patients after curative resection. The nomogram can identify three subgroups of patients at different risks, which showed different prognostic outcomes both in the training cohort and validation cohort. Conclusion We successfully established a reliable and insightful nomogram to predict OS for young non-metastatic RC patients after curative resection. The nomogram may provide accurate prognosis prediction to guide individualized follow-up and treatment plans.

Purpose: Secreted protein acidic and rich in cysteine (SPARC) is an extracellular glycoprotein overexpressed in various malignancies, including esophageal squamous cell carcinoma (ESCC), and is involved in tumor development and progression. This study was initially designed to investigate the biological roles of SPARC in ESCC cell lines by silencing SPARC expression. Methods: The expression of SPARC was examined in eight human ESCC cell lines. Eca109 and HKESC cell lines with high SPARC expression were selected and transiently transfected with SPARC-targeted small interfering RNAs (siRNAs) and subsequently evaluated its impact on cell proliferation, migration and invasion in vitro, as well as the underlying mechanism. Results: Knockdown of SPARC by the specified siRNAs in Eca109 and HKESC cell lines resulted in dramatically downregulation of SPARC expression, and significantly decreased cell migration and invasion involving epithelial-mesenchymal transition (EMT) in vitro. Moreover, SPARC-targeted siRNA reduced the activation of phosphorylated focal adhesion kinase (p-FAK) and extracellular regulated protein kinase (p-ERK). Furthermore, downregulation of either FAK or SPARC expression with specified siRNAs inhibited the phosphorylation of ERK and inhibited cell migration and invasion. However, decreased SPARC expression showed no impact on cell proliferation, survival or apoptosis of Eca109 and HKESC cells when comparing to control transfected groups. Conclusions: These results demonstrated that downregulation of SPARC could decrease cell migration and invasion involving EMT via the p-FAK/p-ERK pathway that might serve as a novel therapeutic target against ESCC.

Background: This study was initially designed to examine whether oxaliplatin-based regimen was superior to cisplatin-based regimen in tumour remission as first-line chemotherapy for advanced gastric cancer (GC). Methods: Literature in EMBASE, PUBMED, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, American Society of Clinical Oncology (ASCO) and European Society of Medical Oncology (ESMO) was searched. Only phase II or III randomized controlled trials (RCTs) comparing the effectiveness and safety between oxaliplatin-based and cisplatin-based regimens as first-line treatment for advanced GC were selected. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. The primary endpoints were complete remission rate (CRR), partial remission rate (PRR), objective response rate (ORR), and disease control rate (DCR). The second endpoint was the toxicity response. Results: 2,140 patients from six phase II or III RCTs were included. Compared to cisplatin-based therapy, subjects who received oxaliplatin-based treatment had significantly higher PRR (OR: 1.25, 95%CI: 1.05-1.48, P=0.01, I2=0%), ORR (OR: 1.21, 95%CI: 1.02-1.44, P=0.03, I2=0%) and DCR (OR: 1.76, 95%CI: 1.31-2.38, P=0.0002, I2=25%), but not CRR (OR: 0.70, 95%CI: 0.37-1.31, P=0.27, I2=0%). In addition, oxaliplatin-based therapy significantly decreased all grades of leukopenia, neutropenia, anemia, febrile neutropenia, nausea, stomatitis, creatinine elevation and thromboembolism, as well as grades 3-4 of leukopenia, neutropenia, anemia and febrile neutropenia than cisplatin-based regimen. However, oxaliplatin-based treatment strikingly increased the risk of thrombocytopenia, sensory neuropathy, diarrhea, fatigue and liver dysfunction. Conclusions: Oxaliplatin-based regimen is superior to cisplatin-based regimen in tumour remission as first-line chemotherapy for advanced GC, and is associated with less toxicity and better tolerability.

Background DNA aneuploidy has attracted growing interest in clinical practice. Nevertheless, its prognostic value in gastric cancer patients remains controversial. This meta-analysis aims to explore the impact of DNA ploidy status on the survival of gastric cancer patients. Methods We used PubMed and Web of Science databases to retrieve relevant articles. The correlation between DNA aneuploidy and the clinicopathological features of gastric cancer, such as stage, depth of invasion (T), lymph node metastasis (N), distant metastasis (M), differentiation (G), tumor types (Lauren classification) and overall survival (OS) were evaluated. Hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) were collected carefully from each article OS was presented with HRs. The relationships between DNA aneuploidy and each characteristic were analyzed using risk ratios (RR) and a 95% confidence interval (CI). Significance was established using P

Serum lactate dehydrogenase (LDH) level is predictive of prognosis in various malignancies. Nevertheless, the association between the prognosis of patients with advanced triple-negative breast cancer (TNBC) and LDH is not well understood. This explorative and retrospective study was conducted to clarify the issue. We found that abnormal baseline LDH levels (> 250 IU/L) were significantly associated with age (> 40 y vs. ≤ 40 y, OR: 0.383, P = 0.031) and number of metastatic sites (2 vs. 1, OR: 4.619, P = 0.006; ≥ 3 vs. 1, OR: 4.727, P = 0.002). The progression-free survival (PFS) of patients with post-treatment LDH higher than baseline (Group 1) was significantly shorter than that in patients with LDH decreased to normal (Group 3) and those with normal baseline and post-treatment LDH (Group 4) (Group 3 vs. Group 1, HR: 0.517, P = 0.038; Group 4 vs. Group 1, HR: 0.346, P < 0.001). Overall survival (OS) in patients with abnormal baseline LDH was significantly shorter than in patients with normal baseline LDH (abnormal vs. normal, HR: 2.073, P < 0.001). Patients whose post-treatment LDH decreased to normal had the most objective response (complete and partial responses) rate after first-line chemotherapy (Group 3 vs. Group 1, OR: 0.074, P < 0.001). In this exploratory analysis, baseline LDH levels associated with OS, while LDH changes after first-line chemotherapy associated with PFS and the chemotherapeutic response. These results show that LDH may have important prognostic value for the survival and chemotherapeutic response in patients with advanced TNBC.

This paper presents a novel decomposition strategy for solving large scale refinery scheduling problems. Instead of formulating one huge and unsolvable MILP or MINLP for centralized problem, we propose a general decomposition scheme that generates smaller sub-systems that can be solved to global optimality. The original problem is decomposed at intermediate storage tanks such that inlet and outlet streams of the tank belong to the different sub-systems. Following the decomposition, each decentralized problem is solved to optimality and the solution to the original problem is obtained by integrating the optimal schedule of each sub-systems. Different case studies of refinery scheduling are presented to illustrate the applicability and effectiveness of the proposed decentralized strategy. The conditions under which these two types of optimization strategies (centralized and decentralized) give the same optimal result are discussed.

Powder feeding is a fundamental unit operation in the pharmaceutical industry. For the cases in which first-principle process models are unknown, such as when new powder mixture feeding operations are being evaluated, or no longer accurately describe current operating behavior, surrogate model-based approaches can be employed in order to quantify input–output behavior. In this work, two such metamodeling techniques—kriging and response surface methods—are used to predict a loss-in-weight feeder unit’s flow variability in terms of unit flowability and feed rate. Based on a comparison of predicted with experimental values, an iteratively constructed kriging model is found to more accurately capture the feeder system behavior compared with the response surface methodology. Although feeders are used as a case study in this paper, the kriging methodology is general to address other processes where first-principle models are not available.

A generalized model is proposed for the continuous time scheduling problem of fluid transfer in tanks. This model generally and more robustly handles the synchronization of time events with material balances than previous proposed models in the literature. A novel method for representing the flow to and from a tank is developed with the potential for significant reduction in the number of necessary time events required for continuous time scheduling formulations. The problem involves the optimal operation of fluid transfer from input sources to tanks, transfer between the tanks, and the transfer from tanks to output destinations. An efficient mixed-integer nonlinear programming formulation is developed based on continuous representation of time domain under the assumption of no simultaneous input and output flow to a tank for fluid streams comprised of multiple components. The new modeling paradigm is applied to examples from the literature for developing refinery crude unit charging schedules.

The problem of short-term scheduling under uncertainty is addressed in this paper through a multiobjective optimization framework that incorporates economic expectation, robustness, and flexibility in terms of demand satisfaction. In order to be able to identify Pareto optimal solutions, a new approach is applied which is based on normal boundary intersection (NBI) technique. The main advantage of this technique is that it avoids the selection of arbitrary parameters and generates a set of evenly distributed set of points independent of the scales of the objectives. Utilizing this idea, alternative schedules are generated that represent trade-off between the various objectives in the face of uncertainty. The approach is illustrated through three case studies and the special characteristics of the scheduling problems are discussed.

A systematic framework is developed to solve the parametric mixed integer linear programming (pMILP) problems where uncertain parameters are present on the righthand side (RHS) of the constraints. For the case of multiple uncertain parameters, a new algorithm of multiparametric linear programming (mpLP) is proposed, which solves a number of nonlinear problems (NLP) iteratively. At each iteration, a point at which the objective value cannot be represented by the current optimal functions is found, and the new optimal function is included in the next iteration. Given the range of uncertain parameters in a MILP problem, the output of this proposed framework is a set of optimal integer solutions and their corresponding critical regions and optimal functions. A number of examples are presented to illustrate the applicabilities of the proposed approach and comparison with existing techniques. © 2006 American Institute of Chemical Engineers AIChE J, 52: 2486 –2495, 2006

The problem addressed in this work is to develop a comprehensive mathematical programming model for the efficient scheduling of oil-refinery operations. Our approach is first to decompose the overall problem spatially into three domains: the crude-oil unloading and blending, the production unit operations and the product blending and delivery. In particular, the first problem involves the crude-oil unloading from vessels, its transfer to storage tanks and the charging schedule for each crude-oil mixture to the distillation units. The second problem consists of the production unit scheduling which includes both fractionation and reaction processes and the third problem describes the finished product blending and shipping end of the refinery. Each of those sub-problems is modeled and solved in a most efficient way using continuous time representation to take advantage of the relatively smaller number of variables and constraints compared to discrete time formulation. The proposed methodology is applied to realistic case studies and significant computational savings can be achieved compared with existing approaches.

The aim of this paper is to develop an integrated framework within which to address the issue of uncertainty in short-term scheduling. The idea of inference-based sensitivity analysis for MILP problems is utilized within a branch-and-bound solution framework to determine the importance of different parameters and constraints and to provide a set of alternative schedules for the range of uncertain parameters under consideration. Two case studies are presented to illustrate the application of the proposed approach and to provide a comparison with parametric programming and robust optimizaton studies.

This paper addresses the problem of crude-oil short-term scheduling, which is the first part of the overall refining operations. The problem involves the optimal operation of crude-oil unloading from vessels, its transfer to storage tanks, and the charging schedule for each crude-oil mixture to the distillation units. A novel model is developed based on a continuous-time representation and results in a mixed-integer linear programming (MILP) problem. The state-task network representation is used throughout this paper. The proposed formulation is applied to several case studies and leads to fewer binary and continuous variables and fewer constraints compared with existing discrete-time models. Thus, an efficient solution can be achieved using available MILP solvers.

Oil refineries are increasingly concerned with improving the planning of their operations and optimizing not only single production units but the whole production enterprise. However, the modeling of the overall refinery operation from the crude oil arrival to the distribution of oil products gives rise to intractable mathematical models. Thus, decomposition methodologies have long been recognized as the major avenue to overcome this computational burden. Following the spatial decomposition which distinguishes the receiving, producing, and delivery end of the refinery, the problem of gasoline blending and distribution is addressed in this paper. The problem involves the optimal operation of gasoline blending, the transfer to productstock tanks, and the delivering schedule to satisfy all of the orders. An efficient mixed-integer linear programming formulation is developed based on continuous representation of the time domain. The assumption of constant recipes is used for the blending stage. The formulatio...

This article presents a comparative review on the operability and flexibility concepts and their application to process design and control. First, the operability and flexibility methodologies are summarized. Then the application of the operability framework to steady-state and dynamic systems is illustrated through the examination of several example categories such as linear and nonlinear, square and non-square systems. The flexibility approach based on the active set strategy is used to study the same examples from the flexibility point of view. The discussed results show that the operability and flexibility approaches examine a process from different perspectives and provide valuable complementary information.

In this paper, a novel framework is developed to deal with multiple uncertain parameters on the right-hand-side (RHS) that can vary independently. The issue is also addressed using parametric mixed integer linear programming (pMILP) analysis where uncertain parameters are present on the right hand side (RHS) of the constraints. For the case of multiple uncertain parameters, a new algorithm of multiparametric linear programming (mpLP) is proposed that does not require the construction of the LP tableaus but relies on the comparison between solutions at leaf nodes. Given the range of uncertain parameters, the output of this proposed framework is a set of optimal integer solutions and their corresponding critical regions and optimal functions

The aim of this paper is to develop an integrated framework in order to address the issue of uncertainty in short-term scheduling. The idea of inference-based sensitivity analysis for MILP problem is employed within a branch and bound solution framework to determine the importance of different parameters and constraints and to provide a set of alternative schedules for the range of uncertain parameters under consideration. An illustrative example is considered using the proposed approach and the results are compared with parametric programming and robust optimization